معرفی
Carlos Alberto Barrera Diaz is a researcher affiliated with the University of Skövde, specifically within the School of Engineering Science and its Production and Automation Engineering department. His work focuses on Reconfigurable Manufacturing Systems (RMS), where he applies simulation-based multi-objective optimization (SMO) and knowledge discovery methods to address challenges in dynamic production environments.
His research spans from 2020 to 2023 and involves collaborations with academics like Amir Nourmohammadi, Henrik Smedberg, and Amos H.C. Ng. Key themes include RMS availability analysis, buffer capacity allocation, task assignment to workstations, and knowledge-driven optimization techniques. Publications in journals such as Mathematics, IEEE Access, and MDPI highlight his technical expertise and contributions to industry-specific challenges like mass customization and Industry 5.0 sustainability goals.
His findings demonstrate how SMO methods can enhance decision-making in RMS, particularly under fluctuating production volumes and capacity constraints. He has been funded by the Knowledge Foundation (KKS) through the Virtual Factories with Knowledge-Driven Optimization (VF-KDO) profile and acknowledged support from VINNOVA’s EXPLAIN project. While no direct student advising is mentioned, his work has significantly advanced RMS optimization frameworks through industrial case studies and algorithmic innovations.